1. Technical Field
The present invention relates generally to integrated circuit design, and more particularly, to determining the critical area of an integrated circuit layout using Voronoi diagrams and shape biasing.
2. Related Art
The “critical area” of a very large scale integrated (VLSI) circuit layout is a measure that reflects the sensitivity of the layout to defects occurring during the manufacturing process. Critical area is widely used to predict the yield of a VLSI chip. Yield prediction is essential in today's VLSI manufacturing due to the growing need to control cost. Models for yield estimation are based on the concept of critical area which represents the main computational problem in the analysis of yield loss due to spot defects during fabrication. Spot defects are caused by particles such as dust and other contaminants in materials and equipment and are classified into two types: First, “extra material” defects cause shorts between different conducting regions by causing shapes to print slightly larger. Second, “missing material” defects create open circuits by causing shapes to print slightly smaller. Extra material defects are the ones that appear most frequently in a typical manufacturing process and are the main reason for yield loss. The difference in size between what is printed and what was intended to print is referred to as “shape bias,” and may result in an enlargement or a shrinkage of the intended printed shapes.
The two most important methods for determining critical area are a Monte Carlo approach and a Voronoi approach. Several other methods to compute critical area have been proposed, but they generally involve long processing time. In the Monte Carlo approach, critical area is approximated by randomly simulating defects on the actual layout having varying sizes. Shape bias is modeled by applying a preprocessing step to expand or shrink all shapes in a level. Processing is then performed on the level. One problem with the Monte Carlo approach, however, is that it is extremely time and resource consuming. In particular, where shape biasing is implemented, the preprocessing step is very expensive and wasteful, particularly where not all shapes are used in the analysis. The Voronoi approach constructs Voronoi diagrams based on the layout geometry, which can be used to compute an exact critical area rather than an approximation. The Voronoi approach is also preferred because it is significantly faster than the Monte Carlo approach, i.e., it operates based on O(N log N). A sampling and/or statistical technique can be implemented with the Voronoi approach, which may make it about 60 times faster than the Monte Carlo approach. However, shape biasing is not used with the Voronoi approach.
In view of the foregoing, there is a need in the art for shape biasing for critical area computation using a Voronoi approach.